Edge detection for 3D point clouds via locally max-angular gaps descriptor

Abstract Numerous point-cloud-based applications, including surface reconstruction and completion, greatly benefit from the utilization of edge features, which play a crucial role in structuring the target shape. Nevertheless, the current limitations of point cloud edge detection techniques in effectiveness and efficiency have motivated us to develop a more robust 3D edge detection method. In this paper, we introduce a simple yet effective descriptor for evaluating the edge level of 3D point clouds. This descriptor is defined as the variance of the max-angular gaps between the target point and its k nearest neighboring points. Leveraging this descriptor, we further propose a multiscale feature fusion strategy that well detect the edge features for the target point cloud. Experimental results demonstrate the superiority of our method over other comparison methods in terms of effectiveness and stability in preserving point cloud edges. Moreover, we investigate the applications of our method in point cloud simplification and registration to provide additional insights into its potential use in some point-cloud-based downstream tasks.